
Explore Apache Spark and the sparklyr package in R to connect to a Spark cluster, run distributed data science tasks, perform exploratory analysis, and generate features on a Kaggle dataset.
Explore spark and sparklyr, learn how Spark provides fast distributed data processing and how sparklyr enables interaction with Spark SQL and R in a cluster managed by Yarn or Mesos.
Explore Sparklyr deployment options, including two well-supported modes with local deployment for learning and an experimental remote option via an open source rest service for Apache Spark.
Demonstrate running spark and the studio server in cloud-based cluster mode with sparklyr, connecting to spark, and provisioning on Amazon while ingestions flow from S3 into HFS.
Explore Livy-based connections for sparklyr from a local studio to a remote spark cluster, with quickstart guidance for Amazon Web Services setup and Livy installation.
Update and install sparklyr in RStudio, connect to spark locally, set up a spark connection reference, and transfer iris data to spark for basic exploration in the spark explorer window.
Explore sparklyr basics through an annotated code walkthrough of iris data, including partitioning into training and testing sets, building a decision tree, predicting species, and evaluating model performance with plots.
Learn dplyr basics in sparklyr by filtering the nyc flights data and comparing local versus spark context. Use show_query and copy_to to inspect queries and create spark tables.
Explore dplyr basics with sparklyr: arrange for sorting, select helpers like starts_with and ends_with, and summarize techniques for missing values using filter and pipe workflows.
Explore how to compute mean, min, max, and standard deviation with summarize in sparklyr, handle median with approximate sql queries, and collect results into a local data frame.
Learn lazy execution in sparklyr, delaying queries with directed graphs for low latency iterative workflows, and master compute, collect, and DBI fetch to control results.
Discover programming in dplyr for sparklyr, mastering nonstandard evaluation and referential transparency, and using unquoting with !! to apply mean, min, max, and standard deviation.
Explore extending sparklyr with the replyr package to address limitations when working with remote data sources, and see how replyr provides summary helpers for iris.
Use sparklyr to work with a 3-million-order instacart open dataset, practicing table joins and distributed computation, and explore big data thresholds, scaling from spark local to larger compute, Kaggle-ready.
Conduct a basic exploratory analysis of snack inventory and orders using Sparklyr, joining departments, aisles, and products to identify top snacks by orders and visualize trends.
Generate machine learning features by transforming insta cart orders into a one-row-per-order format and a sparse aisle indicator matrix with aisles as columns, using R and sparklyr.
Generate indicator features from a product orders data frame using a features list, with nested test and summarize steps, then group by order id and apply sparklyr workflows.
Gain an introduction to Apache Spark with sparklyr, covering Spark components, deployment modes, data management, lazy execution, dplyr verbs, joins, nonstandard evaluation, and exploratory analysis for a machine learning project.
Unlock the full potential of data science and elevate your career with our comprehensive, hands-on course: "Data Science: Sparklyr Basics for Beginners." In this course, you will embark on an exciting learning journey through the world of big data, powered by Apache Spark, one of the most transformative tools in the data science and analytics ecosystem.
Apache Spark is revolutionizing the way we handle distributed data applications. By mastering Spark through the powerful sparklyr package in R, you’ll gain the skills and confidence to tackle complex, large-scale data projects and build data-driven solutions that can make a real impact across industries. Whether you're a complete beginner or looking to deepen your existing knowledge, this course is designed to provide a strong foundation in the world of big data analytics.
What You'll Learn:
Data Frame Manipulation with R and Spark: Master the fundamentals of working with data frames in both R and Spark, and learn the techniques for handling large datasets that will allow you to manage and process data like a professional.
Exploratory Data Analysis (EDA) with Spark: Dive into the process of exploratory data analysis in Spark, using the versatile sparklyr package to conduct insightful data exploration and uncover valuable trends, patterns, and relationships.
Connecting to Spark (Local & Remote): Learn how to easily connect to Spark clusters, both locally and remotely, and efficiently interact with Spark's powerful distributed system to scale your analyses.
Big Data Product Development in R: Discover how to design and build sophisticated data products in R that can efficiently handle and process massive datasets without the limitations of local storage.
Advanced Data Analysis with Spark SQL: Explore how to engage with Apache Spark's SQL capabilities and how to seamlessly integrate sparklyr with Spark SQL to run complex queries and advanced analytics on big data.
Why This Course is Right for You:
This course is not just about learning how to use Spark; it’s about mastering a powerful tool that is used by data scientists and engineers in some of the world’s largest tech companies and industries. Upon completing this course, you will:
Gain a thorough understanding of how to use Apache Spark and the sparklyr package to perform efficient, large-scale data analysis.
Be able to handle, analyze, and visualize big data, regardless of its size or complexity.
Develop hands-on experience that will make you job-ready, with the skills to work on real-world data science projects.
Learn how to integrate Spark into your day-to-day workflows, opening doors to advanced roles in data science, machine learning, and AI.
Who Should Enroll:
Aspiring Data Scientists: Whether you’re new to data science or looking to sharpen your skills, this course offers a clear and structured path to mastering Spark with R.
Data Analysts & Engineers: If you work with large datasets and want to learn the best practices for distributed computing and data analysis, this course is perfect for you.
R Enthusiasts: If you're already proficient in R and want to expand your knowledge into the world of big data analytics, this course will help you seamlessly integrate Spark into your skill set.
Students & Professionals: Anyone looking to break into the field of data science, machine learning, or AI will benefit from this course’s practical, real-world approach to using Spark.
What You’ll Get:
High-Quality Content: Access to detailed lessons with real-world examples that will equip you with actionable skills.
Hands-On Projects: Work on practical exercises and real datasets that mirror challenges faced in today’s data science jobs.
Comprehensive Resources: Downloadable code samples, step-by-step guides, and additional learning materials that will support you in your journey to mastering Spark.
Lifetime Access: Enroll once and return to the material as many times as you need to reinforce your learning.
By the End of the Course, You’ll Be Able to:
Confidently navigate the Spark environment and leverage its powerful tools for big data analytics.
Efficiently analyze large datasets using sparklyr and Spark SQL, uncovering insights and trends with ease.
Build scalable, data-driven products using R and Spark, solving complex problems and contributing to innovative solutions in any industry.
Don’t miss this opportunity to advance your data science career. Enroll now and take the first step towards mastering one of the most sought-after technologies in data science today. Let’s unlock the power of big data and shape the future together!
Enroll today and start your journey to becoming a Spark expert!